Artificial neural network based estimation of global and diffuse fraction of solar radiation using meteorological parameters

Measured meteorological parameters such as air temperature and relative humidity values recorded between 1998 and 2002 for Abha city in Saudi Arabia were used for the estimation of global solar radiation (GSR) and fraction of diffuse solar radiation (DSR) in future time domain using artificial neural network method. The estimations of GSR and DSR were made using three combinations of data sets namely, (i) day of the year and daily maximum air temperature as inputs and global solar radiation as output, (ii) day of the year and daily mean air temperature as inputs and global solar radiation as output and (iii) time day of the year, daily mean air temperature and relative humidity as inputs and global solar radiation as output. The measured data between 1998 and 2001 was used for training the neural networks while the remaining 240 days' data from 2002 as testing data. The testing data was not used in training the neural networks. Obtained results show that neural networks are well capable of estimating global and diffuse solar radiation from temperature and relative humidity. Hence the methodology can be used for estimating GSR and DSR for locations where only temperature and humidity data are available.